mtarforecast: Forecast for MTAR model

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/mtarforecast.R

Description

Bayesian method to compute forecasts of a MTAR model

Usage

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mtarforecast(regimemodel,h,level = 0.95, chain = FALSE, b = NULL)

Arguments

regimemodel

object of class “regime_model” of the process to forecast, it must have exit 'Chain'. Not NULL

h

numeric type, steps for forecast. Not NULL

level

numeric type, confident interval for estimations. Default 0.95

chain

logical type, if return chains of forecasts. Default TRUE

b

numeric type greater or equal 1, autoregressive order of Ut = (Zt ,Xt). Default NULL meaning 1

Details

Based on the equation of the Multivariate Threshold Autoregressive(MTAR) Model

Y_t= φ^{(j)}_{0}+ ∑_{i=1}^{p_j} φ_{i}^{(j)}Y_{t-i}+ ∑_{i=1}^{q_j}β_{i}^{(j)}X_{t-i} + ∑{i=1}^{d_j}δ_{i}^{(j)}Z_{t-i} +Σ_{(j)}^{1/2} ε_{t} if r_{j-1}< Z_t ≤ r_{j},

where process \{ε_{t}\} is a k-variate independent Gaussian process, \{Y_t\} is k-variate process, \{X_t\} is a ν - variate process. The function implements Bayesian estimation that gives us the forecasts for the response and exogenous variable vectors. The coverage percentages of the forecast intervals and the variability of the predictive distributions.

Value

Return a list type object

forecast

list type object with confident interval, mean value of the forecasts and if the chains if TRUE

tsregime

object of class tsregime of the final Output process with forecast

FNDP

Frobenius norm of the covariance matrix of the predictive distribution for Yt and Ut

Author(s)

Valeria Bejarano vbejaranos@unal.edu.co & Andrey Rincon adrincont@unal.edu.co

References

Calderon, S., & Nieto, F. H. (2021). Forecasting with Multivariate Threshold Autoregressive Models. Revista Colombiana de Estadística, 44(2), 369-383.

See Also

mtarns mtarinipars mtarstr

Examples

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  data = datasim
  parameters = list(l = 2,
                    orders = list(pj = c(1,1)),
                    r = data$Sim$r)
  initial = mtarinipars(tsregime_obj = data$Sim,
                        list_model = list(pars = parameters))

  estimyt = mtarns(ini_obj = initial,niter = 1000,chain = TRUE)

  pred1 = mtarforecast(regimemodel = estimyt,h = 100)

adrincont/BMTAR documentation built on Jan. 22, 2022, 2:09 p.m.